Phenotype classification of zebrafish embryos by supervised learning

Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.

斑马鱼（Zebrafish）正日益被用于评估化学物质的生物学特性，进而成为毒理学与药理学研究的专用工具。化学物质对胚胎存活率及发育的影响，通常由专家通过显微镜观察手动评估，并以多张典型照片进行记录。本文提出一种基于监督式机器学习（supervised machine learning）的图像处理方法，可自动对野生型斑马鱼胚胎的明场图像按缺陷类型进行分类。研究表明，相较于人工分类，自动分类在针对3日龄斑马鱼幼体的本次研究涉及的11种缺陷中，有9种可实现与生物专家共识投票90%至100%的吻合度。对斑马鱼胚胎图像的分析与分类实现自动化后，可减轻生物专家的工作负荷与耗时，并提升该分类流程的可重复性与客观性。